Description: Motor skills of humans and animals are still utterly astonishing when compared to robots. This PhD theme will focus around developmental robotics and robot learning methods to advance the state-of-the-art in robot motor skills.

Developmental robotics offers a qualitatively different approach for controlling humanoid robots than the currently predominant approach based on manually engineered controllers. As a result, despite the significant mechatronic advances in humanoid robot design, the motor skill repertoire of current humanoid robots is mediocre compared to their biological counterparts.

This PhD theme aims to bring forward advances in the quality of robot motor skills towards biological richness. The creation of novel, high-performance, passively-compliant humanoid robots (such as the robot COMAN developed at IIT) offers a significant potential for achieving such advances in motor skills. However, as the bottleneck is not the hardware anymore, the main efforts should be directed towards the software that controls the robot. It is no longer reasonable to use oversimplified models of robot dynamics, because the novel compliant robots possess much richer and more complex dynamics than the previous generation of stiff
robots. Therefore, new solutions should be sought to address the challenge of compliant humanoid robot control. And developmental robotics offers one promising alternative for achieving this.

The PhD theme will explore developing novel robot learning algorithms and methods that allow humanoid robots to easily learn novel skills. At the same time, robots should be capable of natural and robust interaction with people. The focus of the research will be on intelligent exploration techniques, robot learning and human-robot interaction.

Description: The creation of novel, high-performance, passively-compliant humanoid robots (such as the robot COMAN developed by IIT) offers a significant potential for achieving more agile locomotion. At this stage, the bottleneck is not the hardware anymore, but the software that controls the robot. It is no longer reasonable to use over-simplified models of robot dynamics, because the novel compliant robots possess much richer and more complex dynamics than the previous generation of stiff robots. Therefore, a new solution should be sought to address the challenge of compliant humanoid robot control.

In this PhD theme, the use of machine learning and robot learning methods will be explored, in order to achieve novel ways for whole-body compliant humanoid robot control. In particular, the focus will be on achieving agile locomotion, based on robot self-learned dynamics, rather than on pre-engineered dynamics model. The PhD candidates will be expected to develop new algorithms for robot learning and to advance the state-of-the-art in humanoid robot locomotion.

The expected outcome of these efforts includes the realization of highly dynamic bipedal locomotion such as omni-directional walking on uneven surfaces, jumping and running robustly on uneven terrain and in presence of high uncertainties, demonstrating robustness and tolerance to external disturbances, etc. The ultimate goal will be achieving locomotion skills comparable to a 1.5 – 2 year-old child.